117
views
0
recommends
+1 Recommend
1 collections
    4
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Application of Graph Theory to the elaboration of personal genomic data for genealogical research

      research-article

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In this communication a representation of the links between DNA-relatives based on Graph Theory is applied to the analysis of personal genomic data to obtain genealogical information. The method is tested on both simulated and real data and its applicability to the field of genealogical research is discussed. We envisage the proposed approach as a valid tool for a streamlined application to the publicly available data generated by many online personal genomic companies. In this way, anonymized matrices of pairwise genome sharing counts can help to improve the retrieval of genetic relationships between customers who provide explicit consent to the treatment of their data.

          Most cited references9

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Using graph theory to analyze biological networks

          Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.
            Bookmark
            • Record: found
            • Abstract: not found
            • Book: not found

            Graph Theory

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Single nucleotide polymorphisms and recombination rate in humans.

              Levels of heterozygosity for single nucleotide polymorphisms vary by more than one order of magnitude in different regions of the human genome. Regional differences in the rate of recombination explain a substantial fraction of the variation in levels of nucleotide polymorphism, consistent with the widespread action of natural selection at the molecular level.
                Bookmark

                Author and article information

                Contributors
                Journal
                peerj-cs
                PeerJ Computer Science
                PeerJ Comput. Sci.
                PeerJ Inc. (San Francisco, USA )
                2376-5992
                14 October 2015
                : 1
                : e27
                Affiliations
                [1 ]Institute of Chemistry of Organometallic Compounds, Research Area of National Research Council , Pisa, Italy
                [2 ]Department of Civilizations and Forms of Knowledge, University of Pisa , Pisa, Italy
                [3 ]Division of Biological Anthropology, University of Cambridge , Cambridge, United Kingdom
                [4 ]Department of Biological, Geological and Environmental Sciences, University of Bologna , Bologna, Italy
                [5 ]Institute of Sciences and Technology of Information, National Research Council , Pisa, Italy
                [6 ]Department of Biology, University of Pisa , Pisa, Italy
                Article
                cs-27
                10.7717/peerj-cs.27
                58de1270-5877-4b53-b29f-b50d315d7d4e
                © 2015 Palleschi et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                History
                : 29 June 2015
                : 24 September 2015
                Funding
                The authors received no funding for this work.
                Categories
                Computational Biology
                Artificial Intelligence
                Visual Analytics

                Computer science
                DNA analysis,Personal genomics,Genealogy,Ancestry reconstruction,Statistical methods,Graph Theory,Genetic genealogy

                Comments

                Comment on this article